Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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https://doi.org/10.1007/978-3-322-90299-3nformation on location was provided, and no maps were constructed. The model comprised a deep autoencoder and a recurrent neural network. The model was demonstrated to (1) learn to correctly label areas of different shapes and sizes, (2) be capable of adapting to changes in room shape and rearrangem

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https://doi.org/10.1007/978-3-322-90299-3(SOMPAM). In this method, patterns corresponding to the pairs of observation and action are memorized to the SOMPAM, and the brief degree is set to value of the rule. In this research robot learns with the aim of acquiring an action rule that can reach the goal point from the start point with as few

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https://doi.org/10.1007/978-3-322-90299-3nit capable to perform on-line analysis for closed-loop control. Here, we present an ultra-compact and low-power system able to acquire from 32 channels and stimulate independently using both current and voltage. The system has been validated . for rats in the recording of spontaneous and evoked pot

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https://doi.org/10.1007/978-3-322-90445-4telligence argue that sensorimotor prediction is a fundamental building block of cognition. In this paper, we learn the sensorimotor prediction on data captured by a mobile robot equipped with distance sensors. We show that Neural Networks can learn the sensorimotor regularities and perform sensorim

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